A heap-based algorithm with deeper exploitative feature for optimal allocations of distributed generations with feeder reconfiguration in power distribution networks

Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied

Considering different objectives and using powerful optimization methods in the distribution networks reconfiguration by accurately achieving the best network configuration can further improve network performance. In this paper, reconfiguration of radial distribution networks is performed to minimize the power loss, voltage sag, voltage unbalance, and energy not supplied (ENS) of customers using a new intelligent artificial electric field algorithm-pattern search (AEFAPS) method based on the many-criteria optimization approach. The voltage sag and voltage unbalance are defined as power quality indices and the ENS is the reliability index. In this study, the pattern search (PS) algorithm enhances the artificial electric field algorithm’s (AEFA) flexibility search both globally and locally. AEFAPS is applied to determine the decision variables as open switches of the networks considering the objective function and operational constraints. The proposed methodology based on AEFAPS is performed on an unbalanced 33-bus IEEE standard network and a real unbalanced 13-bus network. The reconfiguration problem is implemented in single-criterion and many-criteria optimization approaches to evaluate the proposed methodology’s effectiveness using different algorithms. The single-criterion results demonstrated that some power quality indices might be out of range, while all indices are within the permitted range in the many-criteria optimization approach, proving the effectiveness of the proposed many-criteria reconfiguration with logical compromise between different objectives. The results show that AEFAPS identified the network configuration optimally and different objectives are improved considerably compared to the base network. The results confirmed the superior capability of AEFAPS to obtain better objective values and lower values of losses, voltage sag, voltage unbalance, and ENS compared with conventional AEFA, particle swarm optimization (PSO), and grey wolf optimizer (GWO). Moreover, the better performance of AEFAPS is proved in solving the reconfiguration problem compared with previous studies.

Optimization Configuration of Grid-Connected Inverters to Suppress Harmonic Amplification in a Microgrid

This paper provides insight into the optimal configuration scheme of the grid-connected inverters based on harmonic amplification suppression. The connection of the inverters changes the natural resonance frequencies of the grid. Hence, a reasonable configuration of grid-connected inverters can optimize the impedance distribution and shift the natural resonance frequencies to frequency bands farther away from the harmonic sources. We proposed a scheme of site selection and determination of the number of inverters to suppress harmonic amplification. The resonance frequencies and modal frequency sensitivities (MFSs) were obtained by the resonance modal analysis (RMA). Moreover, the concepts of security region and insecurity region of resonance frequency were illustrated. The grid-connected sites can be obtained by calculating the participation factors (PFs) of the resonance frequencies in the insecurity region. Furthermore, the optimal number was determined by building the Norton equivalent circuit of the inverter and evaluating the output impedance at each frequency. Finally, simulations in Matlab/Simulink based on a modified IEEE-9 bus microgrid were utilized to verify the effectiveness of the proposed scheme.

Renewable Energy Micro-Grid Interfacing: Economic and Environmental Issues

This paper presents a study on the technical, economic, and environmental aspects of renewable energy resources-based distributed generation units (DGs). These units are connected to the medium-voltage network to create a new structure called a microgrid (MG). Renewable energies, especially wind and solar, are the most important generation units among DGs. The stochastic behavior of renewable resources increases the need to find the optimum operation of the MG. The optimal operation of a typical MG aims to simultaneously minimize the operational costs and the accompanied emission pollutants over a daily scheduling horizon. Several renewable DGs are investigated in the MG, consisting of biomass generators (BGs), wind turbines (WTs), and photovoltaics (PV). For the proposed operating strategy of the MG, a recent equilibrium optimization (EO) technique is developed and is inspired by the mass balance models for a control volume that are used to estimate their dynamic and equilibrium states. The uncertainties of wind speed and solar irradiation are considered via the Weibull and Beta-probability density functions (PDF) with different states of mean and standard deviation for each hour, respectively. Based on the developed EO, the hourly output powers of the PV, WT, and BGs are optimized, as are the associated power factors of the BGs. The proposed MG operating strategy based on the developed EO is tested on the IEEE 33-bus system and the practical large-scale 141-bus system of AES-Venezuela in the metropolitan area of Caracas. The simulation results demonstrate the significant benefits of the optimal operation of a typical MG using the developed EO by minimizing the operational costs and emissions while preserving the penetration level of the DGs by 60%. Additionally, the voltage profile of the MG operation for each hour is highly enhanced where the minimum voltage at each hour is corrected within the permissible limit of [0.95–1.05] Pu. Moreover, the active power losses per hour are greatly reduced.